Sarah Read
All projects

Professional work / Enterprise AI enablement

Building an Enterprise AI Learning Community

From practitioner-led learning sessions to a scalable model for sharing AI engineering expertise.

I created the AI Power User Series because engineers needed a way to learn from people already using AI in their daily work. I recruited the sponsors and technical presenters, designed the program, and built the processes to keep it running. Across six sessions from January through June, the series recorded more than 5,800 attendances. After an organizational pause, it returned in October with our strongest measured learning results yet.

Organization
ServiceNow
Role
Program Creator · Learning Strategist · Program Lead
Timeline
January 2026–Present
Focus
Enterprise AI Enablement, Program Strategy, Practitioner Engagement, Learning Impact
5,800+
Cumulative session attendance, January–June 2026
4.76/5
October 2026 CSAT
+27%
October 2026 self-reported learner lift

01 · The challenge

Making AI expertise accessible across engineering

AI capabilities were advancing quickly, but the knowledge required to use them effectively was distributed across individual practitioners and teams.

Traditional training alone couldn't keep pace with the speed of change.

Engineers needed opportunities to see how peers were applying AI tools in real workflows, understand practical tradeoffs, ask questions, and learn from demonstrated experience.

The opportunity was to establish a repeatable, practitioner-led approach to AI enablement that could evolve alongside the technology.

02 · My leadership

Building the program from the ground up

I independently originated the concept and designed the learning model, then built the relationships and operating structure needed to bring it to life. I owned the program vision, design, stakeholder engagement, and operating model; technical presenters contributed their own expertise and demonstrations.

  1. 01 —Program strategy and design

    Conceived the Power User Series, defined its purpose and audience, and developed the recurring learning experience around practical engineering applications of AI.

  2. 02 —Stakeholder influence

    Identified and recruited sponsors, technical subject-matter experts, and practitioner-presenters across the organization, building support and participation through relationships rather than formal reporting authority.

  3. 03 —Experience and program execution

    Established the program structure, presenter preparation, session coordination, learner communications, supporting resources, and follow-up experience.

    Worked with technical presenters who contributed their expertise, demonstrations, and practical examples.

  4. 04 —Measurement and continuous improvement

    Established and used learner feedback, satisfaction, recommendation scores, learning lift, and qualitative insights to evaluate effectiveness and guide improvements to the experience.

03 · Program evolution

A learning experience that evolved with engineering

  1. January–June 2026

    Launch and establish

    Six practitioner-led sessions introduced and explored practical AI engineering applications, including code generation, Claude Code, agentic workflows, testing automation, and AI skills.

    The series established a recurring forum for knowledge sharing and technical discussion.

  2. July–September 2026

    Organizational transition

    The series paused during organizational restructuring.

    No sessions held during this period.

  3. October 2026

    Relaunch with hands-on learning

    The program resumed October 22 with Optimal Token Usage in AI Engineering (Hands-on), led by Tommy Ryan — the seventh delivered session.

    Four guided labs, practical optimization techniques, and extensive live Q&A. The session generated the strongest measured learner satisfaction, recommendation score, and learning lift reported for the series to date.

  4. Beyond October

    Continuing the program

    Seven sessions have been delivered to date, and the Power User Series remains an active program, with future sessions and continued practitioner engagement forming part of its ongoing direction.

04 · Reach and evidence

Demonstrating reach and learning impact

Historical program reach

Program-wide · January–October 2026

Across seven delivered sessions, the series recorded more than 5,800 cumulative attendances and collected over 1,100 survey responses.

Cumulative attendance represents reported participation across sessions, not necessarily unique individuals.

Hands-on session results

October 22, 2026 · Single session

238

Attendees at the 30-minute snapshot

Attendance

88

Live questions

Attendance

+63.2

Net Promoter Score

Survey

97.4%

Learner satisfaction

Survey

38

Unique survey respondents

Survey

The October session incorporated four guided labs that allowed participants to practice token optimization techniques during the learning experience rather than only observing demonstrations.

Survey-based results come from 38 respondents—a response rate of approximately 16% based on the 238-attendee snapshot—and do not represent every attendee. Learner lift is self-reported; it is not a verified change in workplace performance or a financial return.

05 · Community of practice

More than a series of presentations

The Power User Series created a recurring space where experienced engineering practitioners could share working techniques, discuss challenges, and make emerging AI practices more accessible to their peers.

The model drew on expertise already present in the organization rather than relying exclusively on traditional course development—forming an emerging community of practice.

  • Practitioner-led expertise

    Engineering experts demonstrated the approaches, workflows, and lessons they were using in their own work.

  • Practical exchange

    Demonstrations, questions, shared resources, and increasingly hands-on activities gave learners opportunities to engage with the material.

  • Continuous evolution

    Session topics and formats could adapt as tools, engineering priorities, and learner needs changed.

06 · Inside the experience

Selected program artifacts

  • Program impact

    A reporting summary of reach, learner feedback, and learning outcomes.

  • Program evolution

    The progression of topics and experiences from January through October 2026.

  • Hands-on learning

    The October guided labs and learner resources, illustrating the move toward immediate application.

Internal materials are not reproduced here. Selected artifacts can be shared on request.

07 · Organizational value

Creating a repeatable model for AI enablement

The Power User Series demonstrated a practical way to mobilize internal expertise and connect it with engineering learning needs. The program:

  • Established an ongoing model for practitioner-led AI knowledge sharing.
  • Created cross-functional connections among sponsors, technical experts, and learners.
  • Reached a broad engineering audience.
  • Used participant feedback and learning measures to refine the experience.
  • Demonstrated the value of hands-on practice in the October relaunch.

Token cost reductions shown during the October session were presenter examples, not verified organization-wide savings. No productivity or financial gains are claimed.

Reflection

What this experience reinforced

"The most valuable expertise in an organization often already exists within its people. The opportunity is to create the structure, relationships, and experiences that make that expertise easier to share.

Building the Power User Series reinforced for me that enterprise learning innovation isn't always about creating more content. Sometimes it's about connecting the right people, asking better questions, and designing the conditions for knowledge to move across an organization."

Interested in seeing more?

Selected program artifacts and detailed walkthroughs may be available upon request.

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